首页 > 最新文献

Lancet Digital Health最新文献

英文 中文
Mitigating the risk of artificial intelligence bias in cardiovascular care 降低心血管护理中人工智能偏差的风险。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-29 DOI: 10.1016/S2589-7500(24)00155-9
Digital health technologies can generate data that can be used to train artificial intelligence (AI) algorithms, which have been particularly transformative in cardiovascular health-care delivery. However, digital and health-care data repositories that are used to train AI algorithms can introduce bias when data are homogeneous and health-care processes are inequitable. AI bias can also be introduced during algorithm development, testing, implementation, and post-implementation processes. The consequences of AI algorithmic bias can be considerable, including missed diagnoses, misclassification of disease, incorrect risk prediction, and inappropriate treatment recommendations. This bias can disproportionately affect marginalised demographic groups. In this Series paper, we provide a brief overview of AI applications in cardiovascular health care, discuss stages of algorithm development and associated sources of bias, and provide examples of harm from biased algorithms. We propose strategies that can be applied during the training, testing, and implementation of AI algorithms to mitigate bias so that all those at risk for or living with cardiovascular disease might benefit equally from AI.
数字医疗技术可以生成用于训练人工智能(AI)算法的数据,这些数据在心血管医疗服务领域尤其具有变革性。然而,用于训练人工智能算法的数字和医疗保健数据存储库可能会在数据同质化和医疗保健流程不公平的情况下引入偏差。在算法开发、测试、实施和实施后的过程中,也可能引入人工智能偏见。人工智能算法偏差的后果可能相当严重,包括漏诊、疾病分类错误、风险预测错误和治疗建议不当。这种偏差会对边缘化人口群体造成极大影响。在这篇系列论文中,我们简要概述了人工智能在心血管医疗保健中的应用,讨论了算法开发的各个阶段和相关的偏见来源,并提供了有偏见的算法造成伤害的实例。我们提出了可在人工智能算法的训练、测试和实施过程中应用的策略,以减少偏差,从而使所有心血管疾病高危人群或患者都能平等地受益于人工智能。
{"title":"Mitigating the risk of artificial intelligence bias in cardiovascular care","authors":"","doi":"10.1016/S2589-7500(24)00155-9","DOIUrl":"10.1016/S2589-7500(24)00155-9","url":null,"abstract":"<div><div>Digital health technologies can generate data that can be used to train artificial intelligence (AI) algorithms, which have been particularly transformative in cardiovascular health-care delivery. However, digital and health-care data repositories that are used to train AI algorithms can introduce bias when data are homogeneous and health-care processes are inequitable. AI bias can also be introduced during algorithm development, testing, implementation, and post-implementation processes. The consequences of AI algorithmic bias can be considerable, including missed diagnoses, misclassification of disease, incorrect risk prediction, and inappropriate treatment recommendations. This bias can disproportionately affect marginalised demographic groups. In this Series paper, we provide a brief overview of AI applications in cardiovascular health care, discuss stages of algorithm development and associated sources of bias, and provide examples of harm from biased algorithms. We propose strategies that can be applied during the training, testing, and implementation of AI algorithms to mitigate bias so that all those at risk for or living with cardiovascular disease might benefit equally from AI.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Evaluation of risk prediction models to select lung cancer screening participants in Europe: a prospective cohort consortium analysis 评估风险预测模型以选择欧洲肺癌筛查参与者:前瞻性队列联合分析
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-21 DOI: 10.1016/S2589-7500(24)00123-7

Background

Lung cancer risk prediction models might efficiently identify individuals who should be offered lung cancer screening. However, their performance has not been comprehensively evaluated in Europe. We aimed to externally validate and evaluate the performance of several risk prediction models that predict lung cancer incidence or mortality in prospective European cohorts.

Methods

We analysed 240 137 participants aged 45–80 years with a current or former smoking history from nine European countries in four prospective cohorts from the pooled database of the Lung Cancer Cohort Consortium: the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (Finland), the Nord-Trøndelag Health Study (Norway), CONSTANCES (France), and the European Prospective Investigation into Cancer and Nutrition (Denmark, Germany, Italy, Spain, Sweden, the Netherlands, and Norway). We evaluated ten lung cancer risk models, which comprised the Bach, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial 2012 model (PLCOm2012), the Lung Cancer Risk Assessment Tool (LCRAT), the Lung Cancer Death Risk Assessment Tool (LCDRAT), the Nord-Trøndelag Health Study (HUNT), the Optimized Early Warning Model for Lung Cancer Risk (OWL), the University College London—Death (UCLD), the University College London—Incidence (UCLI), the Liverpool Lung Project version 2 (LLP version 2), and the Liverpool Lung Project version 3 (LLP version 3) models. We quantified model calibration as the ratio of expected to observed cases or deaths and discrimination using the area under the receiver operating characteristic curve (AUC). For each model, we also identified risk thresholds that would screen the same number of individuals as each of the US Preventive Services Task Force 2021 (USPSTF-2021), the US Preventive Services Task Force 2013 (USPSTF-2013), and the Nederlands–Leuvens Longkanker Screenings Onderzoek (NELSON) criteria.

Findings

Among the participants, 1734 lung cancer cases and 1072 lung cancer deaths occurred within five years of enrolment. Most models had reasonable calibration in most countries, although the LLP version 2 overpredicted risk by more than 50% in eight countries (expected to observed ≥1·50). The PLCOm2012, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI models showed similar discrimination in most countries, with AUCs ranging from 0·68 (95% CI 0·59–0·77) to 0·83 (0·78–0·89), whereas the LLP version 2 and LLP version 3 showed lower discrimination, with AUCs ranging from 0·64 (95% CI 0·57–0·72) to 0·78 (0·74–0·83). When pooling data from all countries (but excluding the HUNT cohort), 33·9% (73 313 of 216 387) of individuals were eligible by USPSTF-2021 criteria, which included 74·8% (1185) of lung cancers and 76·3% (730) of lung cancer deaths occurring over 5 years. Fewer individuals were selected by USPSTF-2013 and NELSON criteria. After applying thresholds to select a

背景肺癌风险预测模型可有效识别应接受肺癌筛查的人群。然而,欧洲尚未对这些模型的性能进行全面评估。我们的目的是在欧洲前瞻性队列中对几种预测肺癌发病率或死亡率的风险预测模型的性能进行外部验证和评估。方法我们分析了肺癌队列联合会(Lung Cancer Cohort Consortium)集合数据库中四个前瞻性队列中来自九个欧洲国家的 240 137 名 45-80 岁、目前或曾经有吸烟史的参与者,这四个前瞻性队列分别是:α-生育酚、β-胡萝卜素癌症预防研究(芬兰)、北特伦德拉格健康研究(挪威)、CONSTANCES(法国)和欧洲癌症与营养前瞻性调查(丹麦、德国、意大利、西班牙、瑞典、荷兰和挪威)。我们评估了十种肺癌风险模型,包括巴赫模型、前列腺癌、肺癌、结肠直肠癌和卵巢癌筛查试验 2012 模型 (PLCOm2012)、肺癌风险评估工具 (LCRAT)、肺癌死亡风险评估工具 (LCDRAT)、北特伦德拉格健康研究 (HUNT)、肺癌风险评估工具 (LCRAT)、肺癌死亡风险评估工具 (LCDRAT)、肺癌早期预警优化模型 (HUNT)、肺癌风险评估工具 (LCRAT)、肺癌死亡风险评估工具 (LCDRAT)、肺癌风险优化预警模型 (OWL)、伦敦大学学院死亡模型 (UCLD)、伦敦大学学院发病模型 (UCLI)、利物浦肺项目第二版模型 (LLP 第二版) 和利物浦肺项目第三版模型 (LLP 第三版)。我们用预期病例或死亡病例与观察病例或死亡病例之比来量化模型校准,并用接收者工作特征曲线下面积(AUC)来区分模型。对于每个模型,我们还确定了与美国预防服务工作组 2021 年(USPSTF-2021)、美国预防服务工作组 2013 年(USPSTF-2013)和 Nederlands-Leuvens Longkanker Screenings Onderzoek(NELSON)标准相同的筛查人数的风险阈值。在大多数国家,大多数模型都具有合理的校准,但在 8 个国家,LLP 第 2 版对风险的预测超过了 50%(预期与观察值之比≥1-50)。PLCOm2012、LCDRAT、LCRAT、Bach、HUNT、OWL、UCLD 和 UCLI 模型在大多数国家显示出相似的区分度,AUC 从 0-68(95% CI 0-59-0-77)到 0-83(0-78-0-89)不等,而 LLP 版本 2 和 LLP 版本 3 显示出较低的区分度,AUC 从 0-64(95% CI 0-57-0-72)到 0-78(0-74-0-83)不等。在汇总所有国家的数据(但不包括 HUNT 队列)时,33-9% 的患者(216 387 例中的 73 313 例)符合 USPSTF-2021 标准,其中包括 74-8% 的肺癌患者(1185 例)和 76-3% 的 5 年以上肺癌死亡患者(730 例)。根据 USPSTF-2013 和 NELSON 标准,符合条件的人数较少。在应用阈值选择与 USPSTF-2021 相同规模的人群后,PLCOm2012、LCDRAT、LCRAT、Bach、HUNT、OWL、UCLD 和 UCLI 模型识别了 77%-6%-79-1% 的未来病例,尽管与 USPSTF-2021 标准相比,它们选择的个体年龄稍大。USPSTF-2013 和 NELSON 的结果相似。解释:在欧洲国家,几种肺癌风险预测模型表现良好,如果用来代替分类资格标准,可能会提高肺癌筛查的效率。
{"title":"Evaluation of risk prediction models to select lung cancer screening participants in Europe: a prospective cohort consortium analysis","authors":"","doi":"10.1016/S2589-7500(24)00123-7","DOIUrl":"10.1016/S2589-7500(24)00123-7","url":null,"abstract":"<div><h3>Background</h3><p>Lung cancer risk prediction models might efficiently identify individuals who should be offered lung cancer screening. However, their performance has not been comprehensively evaluated in Europe. We aimed to externally validate and evaluate the performance of several risk prediction models that predict lung cancer incidence or mortality in prospective European cohorts.</p></div><div><h3>Methods</h3><p>We analysed 240 137 participants aged 45–80 years with a current or former smoking history from nine European countries in four prospective cohorts from the pooled database of the Lung Cancer Cohort Consortium: the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (Finland), the Nord-Trøndelag Health Study (Norway), CONSTANCES (France), and the European Prospective Investigation into Cancer and Nutrition (Denmark, Germany, Italy, Spain, Sweden, the Netherlands, and Norway). We evaluated ten lung cancer risk models, which comprised the Bach, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial 2012 model (PLCO<sub>m2012</sub>), the Lung Cancer Risk Assessment Tool (LCRAT), the Lung Cancer Death Risk Assessment Tool (LCDRAT), the Nord-Trøndelag Health Study (HUNT), the Optimized Early Warning Model for Lung Cancer Risk (OWL), the University College London—Death (UCLD), the University College London—Incidence (UCLI), the Liverpool Lung Project version 2 (LLP version 2), and the Liverpool Lung Project version 3 (LLP version 3) models. We quantified model calibration as the ratio of expected to observed cases or deaths and discrimination using the area under the receiver operating characteristic curve (AUC). For each model, we also identified risk thresholds that would screen the same number of individuals as each of the US Preventive Services Task Force 2021 (USPSTF-2021), the US Preventive Services Task Force 2013 (USPSTF-2013), and the Nederlands–Leuvens Longkanker Screenings Onderzoek (NELSON) criteria.</p></div><div><h3>Findings</h3><p>Among the participants, 1734 lung cancer cases and 1072 lung cancer deaths occurred within five years of enrolment. Most models had reasonable calibration in most countries, although the LLP version 2 overpredicted risk by more than 50% in eight countries (expected to observed ≥1·50). The PLCO<sub>m2012</sub>, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI models showed similar discrimination in most countries, with AUCs ranging from 0·68 (95% CI 0·59–0·77) to 0·83 (0·78–0·89), whereas the LLP version 2 and LLP version 3 showed lower discrimination, with AUCs ranging from 0·64 (95% CI 0·57–0·72) to 0·78 (0·74–0·83). When pooling data from all countries (but excluding the HUNT cohort), 33·9% (73 313 of 216 387) of individuals were eligible by USPSTF-2021 criteria, which included 74·8% (1185) of lung cancers and 76·3% (730) of lung cancer deaths occurring over 5 years. Fewer individuals were selected by USPSTF-2013 and NELSON criteria. After applying thresholds to select a ","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001237/pdfft?md5=f39b6b82aa1c03f5fab734e0e84a6e1f&pid=1-s2.0-S2589750024001237-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A future role for health applications of large language models depends on regulators enforcing safety standards 大型语言模型未来在健康领域的应用取决于监管机构是否执行安全标准
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-21 DOI: 10.1016/S2589-7500(24)00124-9

Among the rapid integration of artificial intelligence in clinical settings, large language models (LLMs), such as Generative Pre-trained Transformer-4, have emerged as multifaceted tools that have potential for health-care delivery, diagnosis, and patient care. However, deployment of LLMs raises substantial regulatory and safety concerns. Due to their high output variability, poor inherent explainability, and the risk of so-called AI hallucinations, LLM-based health-care applications that serve a medical purpose face regulatory challenges for approval as medical devices under US and EU laws, including the recently passed EU Artificial Intelligence Act. Despite unaddressed risks for patients, including misdiagnosis and unverified medical advice, such applications are available on the market. The regulatory ambiguity surrounding these tools creates an urgent need for frameworks that accommodate their unique capabilities and limitations. Alongside the development of these frameworks, existing regulations should be enforced. If regulators fear enforcing the regulations in a market dominated by supply or development by large technology companies, the consequences of layperson harm will force belated action, damaging the potentiality of LLM-based applications for layperson medical advice.

在人工智能与临床环境的快速融合中,大型语言模型(LLMs),如生成预训练转换器-4,已成为一种多方面的工具,在医疗保健服务、诊断和病人护理方面具有潜力。然而,LLMs 的部署引发了大量的监管和安全问题。由于其输出可变性高、内在可解释性差以及所谓的人工智能幻觉风险,根据美国和欧盟法律(包括最近通过的《欧盟人工智能法案》),基于 LLM 的医疗保健应用在作为医疗设备获得批准时面临监管挑战。尽管患者面临的风险(包括误诊和未经验证的医疗建议)尚未得到解决,但市场上仍有此类应用程序。围绕这些工具的监管模糊性导致迫切需要制定框架,以适应其独特的能力和局限性。在制定这些框架的同时,应执行现有法规。如果监管者害怕在由大型技术公司主导供应或开发的市场中执行法规,那么外行人受到伤害的后果将迫使监管者迟迟不采取行动,从而损害基于 LLM 的外行人医疗建议应用的潜力。
{"title":"A future role for health applications of large language models depends on regulators enforcing safety standards","authors":"","doi":"10.1016/S2589-7500(24)00124-9","DOIUrl":"10.1016/S2589-7500(24)00124-9","url":null,"abstract":"<div><p>Among the rapid integration of artificial intelligence in clinical settings, large language models (LLMs), such as Generative Pre-trained Transformer-4, have emerged as multifaceted tools that have potential for health-care delivery, diagnosis, and patient care. However, deployment of LLMs raises substantial regulatory and safety concerns. Due to their high output variability, poor inherent explainability, and the risk of so-called AI hallucinations, LLM-based health-care applications that serve a medical purpose face regulatory challenges for approval as medical devices under US and EU laws, including the recently passed EU Artificial Intelligence Act. Despite unaddressed risks for patients, including misdiagnosis and unverified medical advice, such applications are available on the market. The regulatory ambiguity surrounding these tools creates an urgent need for frameworks that accommodate their unique capabilities and limitations. Alongside the development of these frameworks, existing regulations should be enforced. If regulators fear enforcing the regulations in a market dominated by supply or development by large technology companies, the consequences of layperson harm will force belated action, damaging the potentiality of LLM-based applications for layperson medical advice.</p></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001249/pdfft?md5=2df13b013a0e89af3fe332b6bcb83ed0&pid=1-s2.0-S2589750024001249-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Balancing AI innovation with patient safety 平衡人工智能创新与患者安全
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-21 DOI: 10.1016/S2589-7500(24)00175-4
{"title":"Balancing AI innovation with patient safety","authors":"","doi":"10.1016/S2589-7500(24)00175-4","DOIUrl":"10.1016/S2589-7500(24)00175-4","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001754/pdfft?md5=0a8eaf69e78527e6cec35e921968cd32&pid=1-s2.0-S2589750024001754-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Reporting standards to support cost-effectiveness evaluations of AI-driven health care 支持人工智能驱动的医疗保健成本效益评估的报告标准
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-21 DOI: 10.1016/S2589-7500(24)00171-7
{"title":"Reporting standards to support cost-effectiveness evaluations of AI-driven health care","authors":"","doi":"10.1016/S2589-7500(24)00171-7","DOIUrl":"10.1016/S2589-7500(24)00171-7","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001717/pdfft?md5=38bf3554f6e2ebc110a0128fd57b4837&pid=1-s2.0-S2589750024001717-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Correction to Lancet Digit Health 2024; 6: e605–13 对《柳叶刀数字健康》的更正 2024; 6: e605-13
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-21 DOI: 10.1016/S2589-7500(24)00176-6
{"title":"Correction to Lancet Digit Health 2024; 6: e605–13","authors":"","doi":"10.1016/S2589-7500(24)00176-6","DOIUrl":"10.1016/S2589-7500(24)00176-6","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001766/pdfft?md5=11bd2424e9355f76c22900c4a010ada2&pid=1-s2.0-S2589750024001766-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142040581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Physiological presentation and risk factors of long COVID in the UK using smartphones and wearable devices: a longitudinal, citizen science, case–control study 英国长期使用智能手机和可穿戴设备的 COVID 的生理表现和风险因素:一项纵向、公民科学、病例对照研究。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-12 DOI: 10.1016/S2589-7500(24)00140-7

Background

The emergence of long COVID as a COVID-19 sequela was largely syndromic in characterisation. Digital health technologies such as wearable devices open the possibility to study this condition with passive, objective data in addition to self-reported symptoms. We aimed to quantify the prevalence and severity of symptoms across collected mobile health metrics over 12 weeks following COVID-19 diagnosis and to identify risk factors for the development of post-COVID-19 condition (also known as long COVID).

Methods

The Covid Collab study was a longitudinal, self-enrolled, community, case–control study. We recruited participants from the UK through a smartphone app, media publications, and promotion within the Fitbit app between Aug 28, 2020, and May 31, 2021. Adults (aged ≥18 years) who reported a COVID-19 diagnosis with a positive antigen or PCR test before Feb 1, 2022, were eligible for inclusion. We compared a cohort of 1200 patients who tested positive for COVID-19 with a cohort of 3600 sex-matched and age-matched controls without a COVID-19 diagnosis. Participants could provide information on COVID-19 symptoms and mental health through self-reported questionnaires (active data) and commercial wearable fitness devices (passive data). Data were compared between cohorts at three periods following diagnosis: acute COVID-19 (0–4 weeks), ongoing COVID-19 (4–12 weeks), and post-COVID-19 (12–16 weeks). We assessed sociodemographic and mobile health risk factors for the development of long COVID (defined as either a persistent change in a physiological signal or self-reported symptoms for ≥12 weeks after COVID-19 diagnosis).

Findings

By Aug 1, 2022, 17 667 participants had enrolled into the study, of whom 1200 (6·8%) cases and 3600 (20·4%) controls were included in the analyses. Compared with baseline (65 beats per min), resting heart rate increased significantly during the acute (0·47 beats per min; odds ratio [OR] 1·06 [95% CI 1·03–1·09]; p<0·0001), ongoing (0·99 beats per min; 1·11 [1·08–1·14]; p<0·0001), and post-COVID-19 (0·52 beats per min; 1·04 [1·02–1·07]; p=0·0017) phases. An increased level of historical activity in the period from 24 months to 6 months preceding COVID-19 diagnosis was protective against long COVID (coefficient –0·017 [95% CI –0·030 to –0·003]; p=0·015). Depressive symptoms were persistently elevated following COVID-19 (OR 1·03 [95% CI 1·01–1·06]; p=0·0033) and were a potential risk factor for developing long COVID (1·14 [1·07–1·22]; p<0·0001).

Interpretation

Mobile health technologies and commercial wearable devices might prove to be a useful resource for tracking recovery from COVID-19 and the prevalence of its long-term sequelae, as well as representing an abundant source of historical data. Mental wellbeing can be impacted negatively for an extended period following COVID-19.

Funding

National

背景:长 COVID 作为 COVID-19 后遗症的出现在很大程度上是综合征的特征。除自我报告的症状外,可穿戴设备等数字健康技术为利用被动、客观的数据研究这一病症提供了可能。我们的目标是量化 COVID-19 诊断后 12 周内收集到的移动健康指标的症状发生率和严重程度,并确定 COVID-19 后遗症(又称长 COVID)发展的风险因素:Covid Collab研究是一项自我注册的纵向社区病例对照研究。我们在 2020 年 8 月 28 日至 2021 年 5 月 31 日期间通过智能手机应用程序、媒体出版物和 Fitbit 应用程序中的推广活动在英国招募参与者。凡在 2022 年 2 月 1 日前报告 COVID-19 诊断且抗原或 PCR 检测呈阳性的成人(年龄≥18 岁)均符合纳入条件。我们将 COVID-19 检测呈阳性的 1200 名患者与未确诊 COVID-19 的 3600 名性别和年龄匹配的对照组进行了比较。参与者可通过自我报告问卷(主动数据)和商用可穿戴健身设备(被动数据)提供有关 COVID-19 症状和心理健康的信息。我们比较了不同组群在确诊后三个时期的数据:急性 COVID-19(0-4 周)、持续 COVID-19(4-12 周)和 COVID-19 后(12-16 周)。我们评估了发生长期 COVID 的社会人口和移动健康风险因素(定义为 COVID-19 诊断后≥12 周内生理信号或自我报告症状的持续变化):截至2022年8月1日,共有17 667名参与者参加了研究,其中1200名(6-8%)病例和3600名(20-4%)对照者被纳入分析。与基线(65次/分钟)相比,急性期静息心率显著增加(0-47次/分钟;几率比[OR] 1-06 [95% CI 1-03-1-09];p解释:移动健康技术和商用可穿戴设备可能会成为跟踪 COVID-19 恢复情况及其长期后遗症流行情况的有用资源,同时也是丰富的历史数据来源。在 COVID-19 之后,心理健康可能会受到长期的负面影响:国家健康与护理研究所(NIHR)、国家健康与护理研究所莫兹利生物医学研究中心(NIHR Maudsley Biomedical Research Centre)、英国研究与创新组织(UK Research and Innovation)和医学研究委员会(Medical Research Council)。
{"title":"Physiological presentation and risk factors of long COVID in the UK using smartphones and wearable devices: a longitudinal, citizen science, case–control study","authors":"","doi":"10.1016/S2589-7500(24)00140-7","DOIUrl":"10.1016/S2589-7500(24)00140-7","url":null,"abstract":"<div><h3>Background</h3><p>The emergence of long COVID as a COVID-19 sequela was largely syndromic in characterisation. Digital health technologies such as wearable devices open the possibility to study this condition with passive, objective data in addition to self-reported symptoms. We aimed to quantify the prevalence and severity of symptoms across collected mobile health metrics over 12 weeks following COVID-19 diagnosis and to identify risk factors for the development of post-COVID-19 condition (also known as long COVID).</p></div><div><h3>Methods</h3><p>The Covid Collab study was a longitudinal, self-enrolled, community, case–control study. We recruited participants from the UK through a smartphone app, media publications, and promotion within the Fitbit app between Aug 28, 2020, and May 31, 2021. Adults (aged ≥18 years) who reported a COVID-19 diagnosis with a positive antigen or PCR test before Feb 1, 2022, were eligible for inclusion. We compared a cohort of 1200 patients who tested positive for COVID-19 with a cohort of 3600 sex-matched and age-matched controls without a COVID-19 diagnosis. Participants could provide information on COVID-19 symptoms and mental health through self-reported questionnaires (active data) and commercial wearable fitness devices (passive data). Data were compared between cohorts at three periods following diagnosis: acute COVID-19 (0–4 weeks), ongoing COVID-19 (4–12 weeks), and post-COVID-19 (12–16 weeks). We assessed sociodemographic and mobile health risk factors for the development of long COVID (defined as either a persistent change in a physiological signal or self-reported symptoms for ≥12 weeks after COVID-19 diagnosis).</p></div><div><h3>Findings</h3><p>By Aug 1, 2022, 17 667 participants had enrolled into the study, of whom 1200 (6·8%) cases and 3600 (20·4%) controls were included in the analyses. Compared with baseline (65 beats per min), resting heart rate increased significantly during the acute (0·47 beats per min; odds ratio [OR] 1·06 [95% CI 1·03–1·09]; p&lt;0·0001), ongoing (0·99 beats per min; 1·11 [1·08–1·14]; p&lt;0·0001), and post-COVID-19 (0·52 beats per min; 1·04 [1·02–1·07]; p=0·0017) phases. An increased level of historical activity in the period from 24 months to 6 months preceding COVID-19 diagnosis was protective against long COVID (coefficient –0·017 [95% CI –0·030 to –0·003]; p=0·015). Depressive symptoms were persistently elevated following COVID-19 (OR 1·03 [95% CI 1·01–1·06]; p=0·0033) and were a potential risk factor for developing long COVID (1·14 [1·07–1·22]; p&lt;0·0001).</p></div><div><h3>Interpretation</h3><p>Mobile health technologies and commercial wearable devices might prove to be a useful resource for tracking recovery from COVID-19 and the prevalence of its long-term sequelae, as well as representing an abundant source of historical data. Mental wellbeing can be impacted negatively for an extended period following COVID-19.</p></div><div><h3>Funding</h3><p>National ","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001407/pdfft?md5=41fde3576005e91fa40bb70d2e644041&pid=1-s2.0-S2589750024001407-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Digital solutions in paediatric sepsis: current state, challenges, and opportunities to improve care around the world 儿科败血症的数字化解决方案:改善全球护理的现状、挑战和机遇。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-12 DOI: 10.1016/S2589-7500(24)00141-9

The digitisation of health care is offering the promise of transforming the management of paediatric sepsis, which is a major source of morbidity and mortality in children worldwide. Digital technology is already making an impact in paediatric sepsis, but is almost exclusively benefiting patients in high-resource health-care settings. However, digital tools can be highly scalable and cost-effective, and—with the right planning—have the potential to reduce global health disparities. Novel digital solutions, from wearable devices and mobile apps, to electronic health record-embedded decision support tools, have an unprecedented opportunity to transform paediatric sepsis research and care. In this Series paper, we describe the current state of digital solutions in paediatric sepsis around the world, the advances in digital technology that are enabling the development of novel applications, and the potential effect of advances in artificial intelligence in paediatric sepsis research and clinical care.

儿科败血症是全球儿童发病和死亡的主要原因之一,医疗保健的数字化为儿科败血症的管理带来了变革的希望。数字技术已经对儿科败血症产生了影响,但几乎只惠及高资源医疗环境中的患者。然而,数字工具具有高度的可扩展性和成本效益,只要规划得当,就有可能缩小全球卫生差距。从可穿戴设备和移动应用程序到嵌入电子健康记录的决策支持工具,各种新颖的数字解决方案为改变儿科败血症研究和护理带来了前所未有的机遇。在这篇系列论文中,我们将介绍全球儿科败血症数字解决方案的现状、数字技术的进步对新型应用开发的推动作用,以及人工智能的进步对儿科败血症研究和临床护理的潜在影响。
{"title":"Digital solutions in paediatric sepsis: current state, challenges, and opportunities to improve care around the world","authors":"","doi":"10.1016/S2589-7500(24)00141-9","DOIUrl":"10.1016/S2589-7500(24)00141-9","url":null,"abstract":"<div><p>The digitisation of health care is offering the promise of transforming the management of paediatric sepsis, which is a major source of morbidity and mortality in children worldwide. Digital technology is already making an impact in paediatric sepsis, but is almost exclusively benefiting patients in high-resource health-care settings. However, digital tools can be highly scalable and cost-effective, and—with the right planning—have the potential to reduce global health disparities. Novel digital solutions, from wearable devices and mobile apps, to electronic health record-embedded decision support tools, have an unprecedented opportunity to transform paediatric sepsis research and care. In this Series paper, we describe the current state of digital solutions in paediatric sepsis around the world, the advances in digital technology that are enabling the development of novel applications, and the potential effect of advances in artificial intelligence in paediatric sepsis research and clinical care.</p></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001419/pdfft?md5=31f8722a8d750546a71029215e4dfdf0&pid=1-s2.0-S2589750024001419-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Effect of wearable activity trackers on physical activity in children and adolescents: a systematic review and meta-analysis 可穿戴活动追踪器对儿童和青少年体育活动的影响:系统综述和荟萃分析。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-06 DOI: 10.1016/S2589-7500(24)00139-0

Background

Physical inactivity in children and adolescents has become a pressing public health concern. Wearable activity trackers can allow self-monitoring of physical activity behaviour and promote autonomous motivation for exercise. However, the effects of wearable trackers on physical activity in young populations remain uncertain.

Methods

In this systematic review and meta-analysis, we searched PubMed, Embase, SPORTDiscus, and Web of Science for publications from database inception up to Aug 30, 2023, without restrictions on language. Studies were eligible if they were randomised controlled trials or clustered randomised controlled trials that examined the use of wearable activity trackers to promote physical activity, reduce sedentary behaviours, or promote overall health in participants with a mean age of 19 years or younger, with no restrictions on health condition or study settings. Studies were excluded if children or adolescents were not the primary intervention cohort, or wearable activity trackers were not worn on users’ bodies to objectively track users’ physical activity levels. Two independent reviewers (WWA and FR) assessed eligibility of studies and contacted authors of studies if more information was needed to assess eligibility. We also searched reference lists from relevant systematic reviews and meta-analyses. Systematic review software Covidence was used for study screening and data extraction. Study characteristics including study setting, participant characteristics, intervention characteristics, comparator, and outcome measurements were extracted from eligible studies. The two primary outcomes were objectively measured daily steps and moderate-to-vigorous physical activity. We used a random-effects model with Hartung–Knapp adjustments to calculate standardised mean differences. Between-study heterogeneity was examined using Higgins I2 and Cochran Q statistic. Publication bias was assessed using Egger's regression test. This systematic review was registered with PROSPERO, CRD42023397248.

Findings

We identified 9619 studies from our database research and 174 studies from searching relevant systematic reviews and meta-analyses, of which 105 were subjected to full text screening. We included 21 eligible studies, involving 3676 children and adolescents (1618 [44%] were female and 2058 [56%] were male, mean age was 13·7 years [SD 2·7]) in our systematic review and meta-analysis. Ten studies were included in the estimation of the effect of wearable activity trackers on objectively measured daily steps and 11 were included for objectively measured moderate-to-vigorous physical activity. Compared with controls, we found a significant increase in objectively measured daily steps (standardised mean difference 0·37 [95% CI 0·09 to 0·65; p=0·013]; Q 47·60 [p<0·0001]; I2 72·7% [95% CI 53·4 to 84·0]), but not for moderate-to-vig

背景:儿童和青少年缺乏运动已成为一个紧迫的公共卫生问题。可穿戴活动追踪器可以对体育锻炼行为进行自我监测,并促进锻炼的自主动力。然而,可穿戴追踪器对青少年体育锻炼的影响仍不确定:在本系统综述和荟萃分析中,我们检索了 PubMed、Embase、SPORTDiscus 和 Web of Science 数据库中从数据库开始到 2023 年 8 月 30 日的出版物,语言不限。符合条件的研究为随机对照试验或分组随机对照试验,这些试验研究了使用可穿戴活动追踪器促进身体活动、减少久坐行为或促进平均年龄在 19 岁或以下的参与者的整体健康,对健康状况或研究环境没有限制。如果儿童或青少年不是主要的干预人群,或者可穿戴活动追踪器没有佩戴在用户身上以客观追踪用户的体力活动水平,则这些研究将被排除在外。两名独立审查员(WWA 和 FR)对研究的资格进行评估,如果需要更多信息来评估资格,则联系研究的作者。我们还检索了相关系统综述和荟萃分析的参考文献目录。我们使用系统综述软件 Covidence 进行研究筛选和数据提取。我们从符合条件的研究中提取了研究特征,包括研究环境、参与者特征、干预特征、比较对象和结果测量。两个主要结果是客观测量的每日步数和中强度体力活动。我们使用了随机效应模型,并进行了 Hartung-Knapp 调整,以计算标准化平均差异。使用 Higgins I2 和 Cochran Q 统计量分析了研究间的异质性。发表偏倚采用 Egger 回归检验进行评估。本系统综述已在 PROSPERO 注册,注册号为 CRD42023397248:我们从数据库研究中确定了 9619 项研究,并通过搜索相关系统综述和荟萃分析确定了 174 项研究,对其中 105 项进行了全文筛选。我们在系统综述和荟萃分析中纳入了 21 项符合条件的研究,涉及 3676 名儿童和青少年(1618 名[44%]为女性,2058 名[56%]为男性,平均年龄为 13-7 岁[SD 2-7])。在评估可穿戴活动追踪器对客观测量的每日步数的影响时,我们纳入了 10 项研究,在客观测量的中强度体力活动方面,我们纳入了 11 项研究。与对照组相比,我们发现客观测量的每日步数有显著增加(标准化平均差 0-37 [95% CI 0-09 至 0-65;p=0-013];Q 47-60 [p2 72-7% [95% CI 53-4 至 84-0]),但中强度体力活动的步数没有增加(-0-08 [-0-18 至 0-02;p=0-11];Q 10-26 [p=0-74];I2 0-0% [0-0 至 53-6]):可穿戴活动追踪器可能会增加不同健康状况的年轻群体的每日步数,但不会增加中等强度的体力活动,这凸显了可穿戴追踪器在激励儿童和青少年体力活动方面的潜力。为了验证我们在步数方面的积极发现,并探索可能的长期效果,有必要进行更严格设计的试验,以尽量减少数据缺失:香港大学教育资助委员会和香港大学基础研究种子基金。
{"title":"Effect of wearable activity trackers on physical activity in children and adolescents: a systematic review and meta-analysis","authors":"","doi":"10.1016/S2589-7500(24)00139-0","DOIUrl":"10.1016/S2589-7500(24)00139-0","url":null,"abstract":"<div><h3>Background</h3><p>Physical inactivity in children and adolescents has become a pressing public health concern. Wearable activity trackers can allow self-monitoring of physical activity behaviour and promote autonomous motivation for exercise. However, the effects of wearable trackers on physical activity in young populations remain uncertain.</p></div><div><h3>Methods</h3><p>In this systematic review and meta-analysis, we searched PubMed, Embase, SPORTDiscus, and Web of Science for publications from database inception up to Aug 30, 2023, without restrictions on language. Studies were eligible if they were randomised controlled trials or clustered randomised controlled trials that examined the use of wearable activity trackers to promote physical activity, reduce sedentary behaviours, or promote overall health in participants with a mean age of 19 years or younger, with no restrictions on health condition or study settings. Studies were excluded if children or adolescents were not the primary intervention cohort, or wearable activity trackers were not worn on users’ bodies to objectively track users’ physical activity levels. Two independent reviewers (WWA and FR) assessed eligibility of studies and contacted authors of studies if more information was needed to assess eligibility. We also searched reference lists from relevant systematic reviews and meta-analyses. Systematic review software Covidence was used for study screening and data extraction. Study characteristics including study setting, participant characteristics, intervention characteristics, comparator, and outcome measurements were extracted from eligible studies. The two primary outcomes were objectively measured daily steps and moderate-to-vigorous physical activity. We used a random-effects model with Hartung–Knapp adjustments to calculate standardised mean differences. Between-study heterogeneity was examined using Higgins <em>I</em><sup>2</sup> and Cochran Q statistic. Publication bias was assessed using Egger's regression test. This systematic review was registered with PROSPERO, CRD42023397248.</p></div><div><h3>Findings</h3><p>We identified 9619 studies from our database research and 174 studies from searching relevant systematic reviews and meta-analyses, of which 105 were subjected to full text screening. We included 21 eligible studies, involving 3676 children and adolescents (1618 [44%] were female and 2058 [56%] were male, mean age was 13·7 years [SD 2·7]) in our systematic review and meta-analysis. Ten studies were included in the estimation of the effect of wearable activity trackers on objectively measured daily steps and 11 were included for objectively measured moderate-to-vigorous physical activity. Compared with controls, we found a significant increase in objectively measured daily steps (standardised mean difference 0·37 [95% CI 0·09 to 0·65; p=0·013]; Q 47·60 [p&lt;0·0001]; <em>I</em><sup>2</sup> 72·7% [95% CI 53·4 to 84·0]), but not for moderate-to-vig","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001390/pdfft?md5=36380a4a62a32c50449fd9f8bf44ceca&pid=1-s2.0-S2589750024001390-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Human mobility patterns in Brazil to inform sampling sites for early pathogen detection and routes of spread: a network modelling and validation study 为早期病原体检测采样点和传播途径提供信息的巴西人口流动模式:网络建模和验证研究。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-08-01 DOI: 10.1016/S2589-7500(24)00099-2

Background

Detecting and foreseeing pathogen dispersion is crucial in preventing widespread disease transmission. Human mobility is a fundamental issue in human transmission of infectious agents. Through a mobility data-driven approach, we aimed to identify municipalities in Brazil that could comprise an advanced sentinel network, allowing for early detection of circulating pathogens and their associated transmission routes.

Methods

In this modelling and validation study, we compiled a comprehensive dataset on intercity mobility spanning air, road, and waterway transport from the Brazilian Institute of Geography and Statistics (2016 data), National Transport Confederation (2022), and National Civil Aviation Agency (2017–23). We constructed a graph-based representation of Brazil's mobility network. The Ford–Fulkerson algorithm was used to rank the 5570 Brazilian cities according to their suitability as sentinel locations, allowing us to predict the most suitable locations for early detection and to track the most likely trajectory of a newly emerged pathogen. We also obtained SARS-CoV-2 genetic data from Brazilian municipalities during the early stage (Feb 25–April 30, 2020) of the virus's introduction and the gamma (P.1) variant emergence in Manaus (Jan 6–March 1, 2021), for the purposes of model validation.

Findings

We found that flights alone transported 79·9 million (95% CI 58·3–101·4 million) passengers annually within Brazil during 2017–22, with seasonal peaks occurring in late spring and summer, and road and river networks had a maximum capacity of 78·3 million passengers weekly in 2016. By analysing the 7 746 479 most probable paths originating from source nodes, we found that 3857 cities fully cover the mobility pattern of all 5570 cities in Brazil, 557 (10·0%) of which cover 6 313 380 (81·5%) of the mobility patterns in our study. By strategically incorporating mobility patterns into Brazil's existing influenza-like illness surveillance network (ie, by switching the location of 111 of 199 sentinel sites to different municipalities), our model predicted that mobility coverage would have a 33·6% improvement from 4 059 155 (52·4%) mobility patterns to 5 422 535 (70·0%) without expanding the number of sentinel sites. Our findings are validated with genomic data collected during the SARS-CoV-2 pandemic period. Our model accurately mapped 22 (51%) of 43 clade 1-affected cities and 28 (60%) of 47 clade 2-affected cities spread from São Paulo city, and 20 (49%) of 41 clade 1-affected cities and 28 (58%) of 48 clade 2-affected cities spread from Rio de Janeiro city, Feb 25–April 30, 2020. Additionally, 224 (73%) of the 307 suggested early-detection locations for pathogens emerging in Manaus corresponded with the first cities affected by the transmission of the gamma variant, Jan 6–16, 2021.

Interpretation

By providing essential clues for effective pathogen

背景:检测和预测病原体的扩散对于预防疾病的广泛传播至关重要。人的流动性是传染病病原体在人际间传播的一个基本问题。通过以流动性数据为驱动的方法,我们旨在确定巴西哪些城市可组成先进的哨点网络,以便及早发现流行病原体及其相关传播途径:在这项建模和验证研究中,我们从巴西地理和统计研究所(2016 年数据)、国家运输联合会(2022 年数据)和国家民航局(2017-23 年数据)中汇编了一个全面的城际交通数据集,涵盖航空、公路和水路运输。我们构建了一个基于图的巴西交通网络表征。我们使用福特-福克森算法,根据巴西 5570 个城市作为哨点地点的适宜性对其进行排序,从而预测出最适合进行早期检测的地点,并追踪新出现病原体的最可能轨迹。为了验证模型,我们还从巴西各城市获得了 SARS-CoV-2 病毒传入初期(2020 年 2 月 25 日至 4 月 30 日)和玛瑙斯出现伽马(P.1)变种(2021 年 1 月 6 日至 3 月 1 日)期间的基因数据:我们发现,在 2017-22 年期间,仅航班每年就在巴西境内运送 7900 万(95% CI 58-300-101-400)人次,季节性高峰出现在春末和夏季,公路和河流网络在 2016 年的最大容量为每周 7800-300 万人次。通过分析源自源节点的 7 746 479 条最可能路径,我们发现 3857 个城市完全覆盖了巴西所有 5570 个城市的流动模式,其中 557 个城市(10-0%)覆盖了我们研究中的 6 313 380 个城市(81-5%)的流动模式。通过战略性地将流动模式纳入巴西现有的流感样疾病监测网络(即把199个哨点中111个哨点的位置转移到不同的城市),我们的模型预测,在不扩大哨点数量的情况下,流动模式的覆盖率将从4 059 155个(52-4%)提高到5 422 535个(70-0%),提高33-6%。我们的研究结果得到了 SARS-CoV-2 大流行期间收集的基因组数据的验证。我们的模型准确绘制了 2020 年 2 月 25 日至 4 月 30 日期间,从圣保罗市扩散的 43 个受 1 支影响城市中的 22 个(51%)和 47 个受 2 支影响城市中的 28 个(60%),以及从里约热内卢市扩散的 41 个受 1 支影响城市中的 20 个(49%)和 48 个受 2 支影响城市中的 28 个(58%)。此外,在马瑙斯出现的病原体的 307 个建议早期检测地点中,有 224 个(73%)与 2021 年 1 月 6 日至 16 日受伽马变异体传播影响的首批城市相对应:我们的研究结果为有效的病原体监测提供了重要线索,有可能为公共卫生政策提供信息,并改善未来的大流行应对工作。我们的研究结果为设计全国范围内的临床样本收集网络提供了以流动性数据为依据的方法,这是一种创新做法,可以改善目前的监测系统:洛克菲勒基金会。
{"title":"Human mobility patterns in Brazil to inform sampling sites for early pathogen detection and routes of spread: a network modelling and validation study","authors":"","doi":"10.1016/S2589-7500(24)00099-2","DOIUrl":"10.1016/S2589-7500(24)00099-2","url":null,"abstract":"<div><h3>Background</h3><p>Detecting and foreseeing pathogen dispersion is crucial in preventing widespread disease transmission. Human mobility is a fundamental issue in human transmission of infectious agents. Through a mobility data-driven approach, we aimed to identify municipalities in Brazil that could comprise an advanced sentinel network, allowing for early detection of circulating pathogens and their associated transmission routes.</p></div><div><h3>Methods</h3><p>In this modelling and validation study, we compiled a comprehensive dataset on intercity mobility spanning air, road, and waterway transport from the Brazilian Institute of Geography and Statistics (2016 data), National Transport Confederation (2022), and National Civil Aviation Agency (2017–23). We constructed a graph-based representation of Brazil's mobility network. The Ford–Fulkerson algorithm was used to rank the 5570 Brazilian cities according to their suitability as sentinel locations, allowing us to predict the most suitable locations for early detection and to track the most likely trajectory of a newly emerged pathogen. We also obtained SARS-CoV-2 genetic data from Brazilian municipalities during the early stage (Feb 25–April 30, 2020) of the virus's introduction and the gamma (P.1) variant emergence in Manaus (Jan 6–March 1, 2021), for the purposes of model validation.</p></div><div><h3>Findings</h3><p>We found that flights alone transported 79·9 million (95% CI 58·3–101·4 million) passengers annually within Brazil during 2017–22, with seasonal peaks occurring in late spring and summer, and road and river networks had a maximum capacity of 78·3 million passengers weekly in 2016. By analysing the 7 746 479 most probable paths originating from source nodes, we found that 3857 cities fully cover the mobility pattern of all 5570 cities in Brazil, 557 (10·0%) of which cover 6 313 380 (81·5%) of the mobility patterns in our study. By strategically incorporating mobility patterns into Brazil's existing influenza-like illness surveillance network (ie, by switching the location of 111 of 199 sentinel sites to different municipalities), our model predicted that mobility coverage would have a 33·6% improvement from 4 059 155 (52·4%) mobility patterns to 5 422 535 (70·0%) without expanding the number of sentinel sites. Our findings are validated with genomic data collected during the SARS-CoV-2 pandemic period. Our model accurately mapped 22 (51%) of 43 clade 1-affected cities and 28 (60%) of 47 clade 2-affected cities spread from São Paulo city, and 20 (49%) of 41 clade 1-affected cities and 28 (58%) of 48 clade 2-affected cities spread from Rio de Janeiro city, Feb 25–April 30, 2020. Additionally, 224 (73%) of the 307 suggested early-detection locations for pathogens emerging in Manaus corresponded with the first cities affected by the transmission of the gamma variant, Jan 6–16, 2021.</p></div><div><h3>Interpretation</h3><p>By providing essential clues for effective pathogen ","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024000992/pdfft?md5=b74d298dd27f122d3107c7fb202b0a16&pid=1-s2.0-S2589750024000992-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141767679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
Lancet Digital Health
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1